Overview

Dataset statistics

Number of variables27
Number of observations839736
Missing cells8992704
Missing cells (%)39.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory173.0 MiB
Average record size in memory216.0 B

Variable types

Categorical5
Numeric18
Unsupported2
Boolean2

Alerts

IR has constant value "-0.33" Constant
full_name has a high cardinality: 839736 distinct values High cardinality
spec_T has a high cardinality: 131 distinct values High cardinality
a is highly correlated with q and 4 other fieldsHigh correlation
e is highly correlated with q and 1 other fieldsHigh correlation
q is highly correlated with a and 6 other fieldsHigh correlation
ad is highly correlated with a and 4 other fieldsHigh correlation
per_y is highly correlated with a and 4 other fieldsHigh correlation
data_arc is highly correlated with n_obs_used and 1 other fieldsHigh correlation
n_obs_used is highly correlated with data_arc and 2 other fieldsHigh correlation
H is highly correlated with q and 4 other fieldsHigh correlation
albedo is highly correlated with n_obs_used and 2 other fieldsHigh correlation
GM is highly correlated with a and 5 other fieldsHigh correlation
BV is highly correlated with albedo and 1 other fieldsHigh correlation
UB is highly correlated with albedo and 1 other fieldsHigh correlation
moid is highly correlated with a and 6 other fieldsHigh correlation
a is highly correlated with adHigh correlation
q is highly correlated with moidHigh correlation
ad is highly correlated with a and 1 other fieldsHigh correlation
per_y is highly correlated with adHigh correlation
data_arc is highly correlated with n_obs_used and 1 other fieldsHigh correlation
n_obs_used is highly correlated with data_arc and 1 other fieldsHigh correlation
H is highly correlated with data_arc and 1 other fieldsHigh correlation
BV is highly correlated with UBHigh correlation
UB is highly correlated with BVHigh correlation
moid is highly correlated with qHigh correlation
a is highly correlated with q and 3 other fieldsHigh correlation
q is highly correlated with a and 2 other fieldsHigh correlation
ad is highly correlated with a and 1 other fieldsHigh correlation
per_y is highly correlated with a and 3 other fieldsHigh correlation
data_arc is highly correlated with n_obs_usedHigh correlation
n_obs_used is highly correlated with data_arc and 1 other fieldsHigh correlation
H is highly correlated with n_obs_used and 1 other fieldsHigh correlation
GM is highly correlated with HHigh correlation
BV is highly correlated with UBHigh correlation
UB is highly correlated with BVHigh correlation
moid is highly correlated with a and 2 other fieldsHigh correlation
neo is highly correlated with extentHigh correlation
spec_B is highly correlated with extentHigh correlation
extent is highly correlated with neo and 2 other fieldsHigh correlation
pha is highly correlated with extentHigh correlation
e is highly correlated with G and 4 other fieldsHigh correlation
G is highly correlated with e and 8 other fieldsHigh correlation
i is highly correlated with e and 3 other fieldsHigh correlation
om is highly correlated with extentHigh correlation
w is highly correlated with extentHigh correlation
q is highly correlated with H and 1 other fieldsHigh correlation
ad is highly correlated with per_yHigh correlation
per_y is highly correlated with adHigh correlation
data_arc is highly correlated with G and 4 other fieldsHigh correlation
n_obs_used is highly correlated with G and 4 other fieldsHigh correlation
H is highly correlated with e and 7 other fieldsHigh correlation
extent is highly correlated with e and 14 other fieldsHigh correlation
albedo is highly correlated with G and 4 other fieldsHigh correlation
GM is highly correlated with G and 5 other fieldsHigh correlation
BV is highly correlated with extent and 3 other fieldsHigh correlation
UB is highly correlated with extent and 4 other fieldsHigh correlation
spec_B is highly correlated with extent and 4 other fieldsHigh correlation
neo is highly correlated with e and 4 other fieldsHigh correlation
pha is highly correlated with extentHigh correlation
moid is highly correlated with q and 1 other fieldsHigh correlation
G has 839617 (> 99.9%) missing values Missing
data_arc has 15789 (1.9%) missing values Missing
diameter has 702055 (83.6%) missing values Missing
extent has 839718 (> 99.9%) missing values Missing
albedo has 703284 (83.8%) missing values Missing
rot_per has 820940 (97.8%) missing values Missing
GM has 839722 (> 99.9%) missing values Missing
BV has 838715 (99.9%) missing values Missing
UB has 838757 (99.9%) missing values Missing
IR has 839735 (> 99.9%) missing values Missing
spec_B has 838070 (99.8%) missing values Missing
spec_T has 838756 (99.9%) missing values Missing
pha has 16922 (2.0%) missing values Missing
moid has 16922 (2.0%) missing values Missing
a is highly skewed (γ1 = -802.1894094) Skewed
ad is highly skewed (γ1 = 240.3784395) Skewed
per_y is highly skewed (γ1 = 445.9490837) Skewed
full_name is uniformly distributed Uniform
extent is uniformly distributed Uniform
full_name has unique values Unique
condition_code is an unsupported type, check if it needs cleaning or further analysis Unsupported
diameter is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-10-01 18:26:40.608893
Analysis finished2022-10-01 18:30:55.577712
Duration4 minutes and 14.97 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

full_name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct839736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
1 Ceres
 
1
(2004 TC8)
 
1
(2004 TT4)
 
1
(2004 TE5)
 
1
(2004 TO5)
 
1
Other values (839731)
839731 

Length

Max length36
Median length18
Mean length18.44594611
Min length9

Characters and Unicode

Total characters15489725
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique839736 ?
Unique (%)100.0%

Sample

1st row 1 Ceres
2nd row 2 Pallas
3rd row 3 Juno
4th row 4 Vesta
5th row 5 Astraea

Common Values

ValueCountFrequency (%)
1 Ceres1
 
< 0.1%
(2004 TC8)1
 
< 0.1%
(2004 TT4)1
 
< 0.1%
(2004 TE5)1
 
< 0.1%
(2004 TO5)1
 
< 0.1%
(2004 TS5)1
 
< 0.1%
(2004 TU5)1
 
< 0.1%
(2004 TY5)1
 
< 0.1%
(2004 TB6)1
 
< 0.1%
(2004 TD6)1
 
< 0.1%
Other values (839726)839726
> 99.9%

Length

2022-10-02T00:00:55.828669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201457156
 
2.5%
201556268
 
2.5%
200552673
 
2.3%
200152365
 
2.3%
200250195
 
2.2%
200649675
 
2.2%
200047772
 
2.1%
200846065
 
2.1%
200739698
 
1.8%
201637468
 
1.7%
Other values (734813)1752937
78.2%

Most occurring characters

ValueCountFrequency (%)
3603869
23.3%
01678719
10.8%
21487025
9.6%
11250155
 
8.1%
(839404
 
5.4%
)839404
 
5.4%
3659532
 
4.3%
4636072
 
4.1%
9595097
 
3.8%
5580224
 
3.7%
Other values (61)3320224
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8339948
53.8%
Space Separator3603869
23.3%
Uppercase Letter1698858
 
11.0%
Open Punctuation839404
 
5.4%
Close Punctuation839404
 
5.4%
Lowercase Letter163391
 
1.1%
Dash Punctuation4742
 
< 0.1%
Other Punctuation100
 
< 0.1%
Modifier Symbol9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S107193
 
6.3%
T103053
 
6.1%
U100348
 
5.9%
R85589
 
5.0%
B77320
 
4.6%
Q76140
 
4.5%
V75183
 
4.4%
C74121
 
4.4%
E73754
 
4.3%
W73654
 
4.3%
Other values (16)852503
50.2%
Lowercase Letter
ValueCountFrequency (%)
a21085
12.9%
e16612
 
10.2%
i14539
 
8.9%
n13416
 
8.2%
r12142
 
7.4%
o12109
 
7.4%
l8890
 
5.4%
s8526
 
5.2%
t7173
 
4.4%
u6444
 
3.9%
Other values (16)42455
26.0%
Decimal Number
ValueCountFrequency (%)
01678719
20.1%
21487025
17.8%
11250155
15.0%
3659532
 
7.9%
4636072
 
7.6%
9595097
 
7.1%
5580224
 
7.0%
6504815
 
6.1%
7474435
 
5.7%
8473874
 
5.7%
Other Punctuation
ValueCountFrequency (%)
'77
77.0%
/18
 
18.0%
.4
 
4.0%
!1
 
1.0%
Space Separator
ValueCountFrequency (%)
3603869
100.0%
Open Punctuation
ValueCountFrequency (%)
(839404
100.0%
Close Punctuation
ValueCountFrequency (%)
)839404
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4742
100.0%
Modifier Symbol
ValueCountFrequency (%)
`9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13627476
88.0%
Latin1862249
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S107193
 
5.8%
T103053
 
5.5%
U100348
 
5.4%
R85589
 
4.6%
B77320
 
4.2%
Q76140
 
4.1%
V75183
 
4.0%
C74121
 
4.0%
E73754
 
4.0%
W73654
 
4.0%
Other values (42)1015894
54.6%
Common
ValueCountFrequency (%)
3603869
26.4%
01678719
12.3%
21487025
10.9%
11250155
 
9.2%
(839404
 
6.2%
)839404
 
6.2%
3659532
 
4.8%
4636072
 
4.7%
9595097
 
4.4%
5580224
 
4.3%
Other values (9)1457975
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15489725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3603869
23.3%
01678719
10.8%
21487025
9.6%
11250155
 
8.1%
(839404
 
5.4%
)839404
 
5.4%
3659532
 
4.3%
4636072
 
4.1%
9595097
 
3.8%
5580224
 
3.7%
Other values (61)3320224
21.4%

a
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct839722
Distinct (%)> 99.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.843216509
Minimum-32588.94299
Maximum3043.149073
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)< 0.1%
Memory size6.4 MiB
2022-10-02T00:00:56.015679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-32588.94299
5-th percentile2.150504217
Q12.385243992
median2.644219113
Q32.996034644
95-th percentile3.19035809
Maximum3043.149073
Range35632.09206
Interquartile range (IQR)0.6107906513

Descriptive statistics

Standard deviation37.32703149
Coefficient of variation (CV)13.12845201
Kurtosis694260.0825
Mean2.843216509
Median Absolute Deviation (MAD)0.2825566375
Skewness-802.1894094
Sum2387545.572
Variance1393.30728
MonotonicityNot monotonic
2022-10-02T00:00:56.423418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.67843892
 
< 0.1%
2.63773642
 
< 0.1%
3.19037682
 
< 0.1%
2.25251122
 
< 0.1%
2.64957852
 
< 0.1%
2.81496242
 
< 0.1%
3.00701932
 
< 0.1%
3.20838312
 
< 0.1%
2.322732
 
< 0.1%
2.59850382
 
< 0.1%
Other values (839712)839714
> 99.9%
ValueCountFrequency (%)
-32588.942991
< 0.1%
-7496.6430361
< 0.1%
-3562.4727751
< 0.1%
-1.2723450071
< 0.1%
0.5552760491
< 0.1%
0.57984967031
< 0.1%
0.5886615311
< 0.1%
0.60085455871
< 0.1%
0.61018591221
< 0.1%
0.6159068111
< 0.1%
ValueCountFrequency (%)
3043.1490731
< 0.1%
1771.0824171
< 0.1%
1603.4425121
< 0.1%
1505.440811
< 0.1%
1498.9978731
< 0.1%
1089.2264291
< 0.1%
1010.3854741
< 0.1%
966.42737421
< 0.1%
914.85720861
< 0.1%
904.11307051
< 0.1%

e
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct839664
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1556327739
Minimum0
Maximum1.201133796
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:56.564782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03968610352
Q10.09145251507
median0.1436543045
Q30.1994001383
95-th percentile0.3023046611
Maximum1.201133796
Range1.201133796
Interquartile range (IQR)0.1079476232

Descriptive statistics

Standard deviation0.09388802373
Coefficient of variation (CV)0.6032664032
Kurtosis7.987223539
Mean0.1556327739
Median Absolute Deviation (MAD)0.05385454964
Skewness1.953826525
Sum130690.443
Variance0.008814961
MonotonicityNot monotonic
2022-10-02T00:00:56.703909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
< 0.1%
0.18016062
 
< 0.1%
0.11177052
 
< 0.1%
0.30377092
 
< 0.1%
0.10207422
 
< 0.1%
0.21753532
 
< 0.1%
0.16927722
 
< 0.1%
0.0967742
 
< 0.1%
0.04191562
 
< 0.1%
0.04408012
 
< 0.1%
Other values (839654)839710
> 99.9%
ValueCountFrequency (%)
08
< 0.1%
3.523002676 × 10-61
 
< 0.1%
3.542565815 × 10-61
 
< 0.1%
3.566871752 × 10-61
 
< 0.1%
3.606057873 × 10-61
 
< 0.1%
4.510673875 × 10-61
 
< 0.1%
6.892931388 × 10-61
 
< 0.1%
9.013926394 × 10-61
 
< 0.1%
9.488363745 × 10-61
 
< 0.1%
1.157942993 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1.2011337961
< 0.1%
1.0010156591
< 0.1%
1.000856061
< 0.1%
1.0001797671
< 0.1%
12
< 0.1%
0.99853566511
< 0.1%
0.99850564431
< 0.1%
0.99737893041
< 0.1%
0.99718402421
< 0.1%
0.99673091251
< 0.1%

G
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct47
Distinct (%)39.5%
Missing839617
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean0.1787394958
Minimum-0.25
Maximum0.6
Zeros1
Zeros (%)< 0.1%
Negative9
Negative (%)< 0.1%
Memory size6.4 MiB
2022-10-02T00:00:56.845507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.25
5-th percentile-0.031
Q10.1
median0.19
Q30.25
95-th percentile0.373
Maximum0.6
Range0.85
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1346027087
Coefficient of variation (CV)0.7530664003
Kurtosis0.9498793195
Mean0.1787394958
Median Absolute Deviation (MAD)0.08
Skewness0.06323698161
Sum21.27
Variance0.01811788919
MonotonicityNot monotonic
2022-10-02T00:00:56.955476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.116
 
< 0.1%
0.196
 
< 0.1%
0.16
 
< 0.1%
0.245
 
< 0.1%
0.255
 
< 0.1%
0.124
 
< 0.1%
0.24
 
< 0.1%
0.164
 
< 0.1%
0.084
 
< 0.1%
0.234
 
< 0.1%
Other values (37)71
 
< 0.1%
(Missing)839617
> 99.9%
ValueCountFrequency (%)
-0.251
< 0.1%
-0.122
< 0.1%
-0.081
< 0.1%
-0.061
< 0.1%
-0.041
< 0.1%
-0.031
< 0.1%
-0.022
< 0.1%
01
< 0.1%
0.012
< 0.1%
0.032
< 0.1%
ValueCountFrequency (%)
0.61
 
< 0.1%
0.511
 
< 0.1%
0.481
 
< 0.1%
0.462
< 0.1%
0.41
 
< 0.1%
0.373
< 0.1%
0.352
< 0.1%
0.341
 
< 0.1%
0.333
< 0.1%
0.323
< 0.1%

i
Real number (ℝ≥0)

HIGH CORRELATION

Distinct839638
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.950009418
Minimum0.007545952591
Maximum175.1887255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:57.084391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.007545952591
5-th percentile1.630090479
Q14.069042633
median7.25731121
Q312.2560259
95-th percentile22.58449192
Maximum175.1887255
Range175.1811795
Interquartile range (IQR)8.186983263

Descriptive statistics

Standard deviation6.666272987
Coefficient of variation (CV)0.7448341869
Kurtosis22.81001748
Mean8.950009418
Median Absolute Deviation (MAD)3.831998472
Skewness2.316527994
Sum7515645.109
Variance44.43919554
MonotonicityNot monotonic
2022-10-02T00:00:57.204967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4492
 
< 0.1%
7.845682
 
< 0.1%
9.720332
 
< 0.1%
13.830612
 
< 0.1%
3.319382
 
< 0.1%
6.21122
 
< 0.1%
11.155922
 
< 0.1%
5.511672
 
< 0.1%
11.137182
 
< 0.1%
3.99712
 
< 0.1%
Other values (839628)839716
> 99.9%
ValueCountFrequency (%)
0.0075459525911
< 0.1%
0.010221355041
< 0.1%
0.011414634331
< 0.1%
0.012466244541
< 0.1%
0.012797626121
< 0.1%
0.01355493321
< 0.1%
0.014578212171
< 0.1%
0.021641561191
< 0.1%
0.021818029981
< 0.1%
0.021854896291
< 0.1%
ValueCountFrequency (%)
175.18872551
< 0.1%
172.87202261
< 0.1%
172.13783581
< 0.1%
170.98831781
< 0.1%
170.91824211
< 0.1%
170.73598821
< 0.1%
170.50970211
< 0.1%
170.32364721
< 0.1%
166.77024191
< 0.1%
165.55634371
< 0.1%

om
Real number (ℝ≥0)

HIGH CORRELATION

Distinct839734
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.5016407
Minimum0.0003882104437
Maximum359.9997998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:57.355010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0003882104437
5-th percentile16.90530558
Q180.21234412
median160.2960655
Q3252.2066452
95-th percentile341.9223183
Maximum359.9997998
Range359.9994116
Interquartile range (IQR)171.9943011

Descriptive statistics

Standard deviation103.0965713
Coefficient of variation (CV)0.6118431304
Kurtosis-1.110165879
Mean168.5016407
Median Absolute Deviation (MAD)84.9337811
Skewness0.1957920987
Sum141496893.8
Variance10628.90302
MonotonicityNot monotonic
2022-10-02T00:00:57.488528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306.291612
 
< 0.1%
254.354082
 
< 0.1%
80.305531571
 
< 0.1%
36.570995641
 
< 0.1%
195.69069221
 
< 0.1%
215.34733711
 
< 0.1%
32.787308831
 
< 0.1%
35.023334381
 
< 0.1%
19.797292471
 
< 0.1%
50.427127461
 
< 0.1%
Other values (839724)839724
> 99.9%
ValueCountFrequency (%)
0.00038821044371
< 0.1%
0.00073491594531
< 0.1%
0.00091659992081
< 0.1%
0.0012460824311
< 0.1%
0.0012481522761
< 0.1%
0.0012499958491
< 0.1%
0.0020917350341
< 0.1%
0.0024331885851
< 0.1%
0.002853313381
< 0.1%
0.0034866336911
< 0.1%
ValueCountFrequency (%)
359.99979981
< 0.1%
359.99978671
< 0.1%
359.99929451
< 0.1%
359.99928631
< 0.1%
359.99863341
< 0.1%
359.99833171
< 0.1%
359.9972211
< 0.1%
359.9971961
< 0.1%
359.99703391
< 0.1%
359.99668181
< 0.1%

w
Real number (ℝ≥0)

HIGH CORRELATION

Distinct839731
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.0765929
Minimum0.001665762699
Maximum359.999833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:57.636086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001665762699
5-th percentile18.09826916
Q191.03955116
median181.6698121
Q3271.5233444
95-th percentile342.2279491
Maximum359.999833
Range359.9981673
Interquartile range (IQR)180.4837933

Descriptive statistics

Standard deviation104.0241103
Coefficient of variation (CV)0.5744757437
Kurtosis-1.202904953
Mean181.0765929
Median Absolute Deviation (MAD)90.22058511
Skewness-0.01433982445
Sum152056533.8
Variance10821.01553
MonotonicityNot monotonic
2022-10-02T00:00:57.762461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292.155372
 
< 0.1%
349.849322
 
< 0.1%
66.070692
 
< 0.1%
292.444152
 
< 0.1%
62.479792
 
< 0.1%
263.29974251
 
< 0.1%
176.79137491
 
< 0.1%
139.38047581
 
< 0.1%
294.21923031
 
< 0.1%
29.860956891
 
< 0.1%
Other values (839721)839721
> 99.9%
ValueCountFrequency (%)
0.0016657626991
< 0.1%
0.001881
< 0.1%
0.0019767619631
< 0.1%
0.0020104829061
< 0.1%
0.0023312991491
< 0.1%
0.0023587290761
< 0.1%
0.0027688758271
< 0.1%
0.0034118870021
< 0.1%
0.0038165259741
< 0.1%
0.0041272129441
< 0.1%
ValueCountFrequency (%)
359.9998331
< 0.1%
359.99955111
< 0.1%
359.999361
< 0.1%
359.99818181
< 0.1%
359.99777631
< 0.1%
359.99732091
< 0.1%
359.99695981
< 0.1%
359.99653611
< 0.1%
359.99612041
< 0.1%
359.99611611
< 0.1%

q
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct839727
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.404732214
Minimum0.07051073204
Maximum80.4241748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:57.904369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.07051073204
5-th percentile1.683260509
Q11.971939116
median2.225494004
Q32.578161876
95-th percentile2.939276732
Maximum80.4241748
Range80.35366406
Interquartile range (IQR)0.6062227602

Descriptive statistics

Standard deviation2.233137563
Coefficient of variation (CV)0.9286429273
Kurtosis247.5557089
Mean2.404732214
Median Absolute Deviation (MAD)0.2939252648
Skewness15.24086049
Sum2019340.211
Variance4.986903376
MonotonicityNot monotonic
2022-10-02T00:00:58.029027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.08927552
 
< 0.1%
2.17910852
 
< 0.1%
2.75667992
 
< 0.1%
2.53418042
 
< 0.1%
2.46746912
 
< 0.1%
1.94528922
 
< 0.1%
2.25007922
 
< 0.1%
2.02150462
 
< 0.1%
2.94876982
 
< 0.1%
2.2366481931
 
< 0.1%
Other values (839717)839717
> 99.9%
ValueCountFrequency (%)
0.070510732041
< 0.1%
0.075871927541
< 0.1%
0.07937929411
< 0.1%
0.079505325591
< 0.1%
0.080744295961
< 0.1%
0.081882150461
< 0.1%
0.08731701491
< 0.1%
0.090797325091
< 0.1%
0.090930267151
< 0.1%
0.092047538421
< 0.1%
ValueCountFrequency (%)
80.42417481
< 0.1%
76.155049751
< 0.1%
65.081901571
< 0.1%
55.848680561
< 0.1%
51.662525841
< 0.1%
51.109841731
< 0.1%
50.785109421
< 0.1%
50.028844511
< 0.1%
49.310681391
< 0.1%
48.746349051
< 0.1%

ad
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct839719
Distinct (%)> 99.9%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.3857068
Minimum0.773683609
Maximum6081.841956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:58.182634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.773683609
5-th percentile2.395914233
Q12.775341696
median3.037745095
Q33.357957635
95-th percentile3.863452183
Maximum6081.841956
Range6081.068273
Interquartile range (IQR)0.5826159394

Descriptive statistics

Standard deviation12.74641907
Coefficient of variation (CV)3.764773452
Kurtosis85017.59278
Mean3.3857068
Median Absolute Deviation (MAD)0.2889413527
Skewness240.3784395
Sum2843079.571
Variance162.4711992
MonotonicityNot monotonic
2022-10-02T00:00:58.315572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.85357452
 
< 0.1%
3.01124792
 
< 0.1%
3.08268752
 
< 0.1%
3.40870552
 
< 0.1%
2.84340422
 
< 0.1%
3.14645322
 
< 0.1%
3.29886782
 
< 0.1%
3.11844822
 
< 0.1%
2.88046362
 
< 0.1%
3.73892052
 
< 0.1%
Other values (839709)839710
> 99.9%
(Missing)6
 
< 0.1%
ValueCountFrequency (%)
0.7736836091
< 0.1%
0.79383942711
< 0.1%
0.80378009791
< 0.1%
0.88165355641
< 0.1%
0.89407925491
< 0.1%
0.89835307611
< 0.1%
0.92828810241
< 0.1%
0.93499390331
< 0.1%
0.93981528041
< 0.1%
0.94055005761
< 0.1%
ValueCountFrequency (%)
6081.8419561
< 0.1%
3539.5182061
< 0.1%
3192.3154111
< 0.1%
2994.0667691
< 0.1%
2974.5483051
< 0.1%
2171.8726851
< 0.1%
1955.6890471
< 0.1%
1924.499921
< 0.1%
1825.6086471
< 0.1%
1784.0841991
< 0.1%

per_y
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct839718
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.859543761
Minimum0
Maximum167877.7127
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:58.582841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.153666412
Q13.68389567
median4.299860013
Q35.185950904
95-th percentile5.698588558
Maximum167877.7127
Range167877.7127
Interquartile range (IQR)1.502055234

Descriptive statistics

Standard deviation252.1725034
Coefficient of variation (CV)36.7622851
Kurtosis256385.4041
Mean6.859543761
Median Absolute Deviation (MAD)0.6773420626
Skewness445.9490837
Sum5760198.98
Variance63590.97146
MonotonicityNot monotonic
2022-10-02T00:00:58.699348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
< 0.1%
4.1888349332
 
< 0.1%
5.6986393092
 
< 0.1%
4.2840574392
 
< 0.1%
5.2990181772
 
< 0.1%
5.7215087142
 
< 0.1%
5.518276922
 
< 0.1%
3.341186122
 
< 0.1%
4.3129397322
 
< 0.1%
3.380715542
 
< 0.1%
Other values (839708)839712
> 99.9%
ValueCountFrequency (%)
05
< 0.1%
0.41378201881
 
< 0.1%
0.4415514591
 
< 0.1%
0.45165485781
 
< 0.1%
0.46576005891
 
< 0.1%
0.47665205211
 
< 0.1%
0.48337113481
 
< 0.1%
0.48538388071
 
< 0.1%
0.48587167091
 
< 0.1%
0.49556926611
 
< 0.1%
ValueCountFrequency (%)
167877.71271
< 0.1%
74536.047511
< 0.1%
64207.874441
< 0.1%
58412.222341
< 0.1%
58037.637731
< 0.1%
35948.887751
< 0.1%
32117.286311
< 0.1%
30044.296321
< 0.1%
27671.848671
< 0.1%
27185.812921
< 0.1%

data_arc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22805
Distinct (%)2.8%
Missing15789
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean5660.187025
Minimum0
Maximum72684
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:58.830020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q13599
median5792
Q37228
95-th percentile10338
Maximum72684
Range72684
Interquartile range (IQR)3629

Descriptive statistics

Standard deviation4192.420393
Coefficient of variation (CV)0.740685842
Kurtosis16.10766265
Mean5660.187025
Median Absolute Deviation (MAD)1751
Skewness2.463619613
Sum4663694119
Variance17576388.75
MonotonicityNot monotonic
2022-10-02T00:00:58.947924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29346
 
1.1%
17932
 
0.9%
36113
 
0.7%
84298
 
0.5%
44247
 
0.5%
54207
 
0.5%
93619
 
0.4%
63358
 
0.4%
73353
 
0.4%
102924
 
0.3%
Other values (22795)774550
92.2%
(Missing)15789
 
1.9%
ValueCountFrequency (%)
01
 
< 0.1%
17932
0.9%
29346
1.1%
36113
0.7%
44247
0.5%
54207
0.5%
63358
 
0.4%
73353
 
0.4%
84298
0.5%
93619
 
0.4%
ValueCountFrequency (%)
726841
< 0.1%
723181
< 0.1%
634311
< 0.1%
626551
< 0.1%
624521
< 0.1%
623291
< 0.1%
621751
< 0.1%
618211
< 0.1%
616991
< 0.1%
615811
< 0.1%

condition_code
Unsupported

REJECTED
UNSUPPORTED

Missing993
Missing (%)0.1%
Memory size6.4 MiB

n_obs_used
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3086
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.6177084
Minimum2
Maximum9325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:59.081599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q150
median118
Q3292
95-th percentile1016
Maximum9325
Range9323
Interquartile range (IQR)242

Descriptive statistics

Standard deviation363.0045479
Coefficient of variation (CV)1.425684608
Kurtosis12.57446619
Mean254.6177084
Median Absolute Deviation (MAD)88
Skewness3.014101542
Sum213811656
Variance131772.3018
MonotonicityNot monotonic
2022-10-02T00:00:59.199673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110352
 
1.2%
109787
 
1.2%
149450
 
1.1%
129420
 
1.1%
158895
 
1.1%
137783
 
0.9%
167227
 
0.9%
96885
 
0.8%
186572
 
0.8%
196028
 
0.7%
Other values (3076)757337
90.2%
ValueCountFrequency (%)
21
 
< 0.1%
3104
 
< 0.1%
4189
 
< 0.1%
5325
 
< 0.1%
61033
 
0.1%
71104
 
0.1%
82176
 
0.3%
96885
0.8%
109787
1.2%
1110352
1.2%
ValueCountFrequency (%)
93251
< 0.1%
84901
< 0.1%
81461
< 0.1%
71041
< 0.1%
66711
< 0.1%
61531
< 0.1%
61021
< 0.1%
60341
< 0.1%
57421
< 0.1%
54751
< 0.1%

H
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9823
Distinct (%)1.2%
Missing2694
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean16.78606863
Minimum-1.1
Maximum33.2
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size6.4 MiB
2022-10-02T00:00:59.331937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.1
5-th percentile14.3
Q115.9
median16.8
Q317.6
95-th percentile19
Maximum33.2
Range34.3
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.821273637
Coefficient of variation (CV)0.1084991178
Kurtosis9.431060785
Mean16.78606863
Median Absolute Deviation (MAD)0.835
Skewness0.6487420593
Sum14050644.46
Variance3.317037662
MonotonicityNot monotonic
2022-10-02T00:00:59.453330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.824112
 
2.9%
16.524016
 
2.9%
16.623694
 
2.8%
16.723659
 
2.8%
16.923575
 
2.8%
16.423160
 
2.8%
1722761
 
2.7%
17.122557
 
2.7%
16.322306
 
2.7%
16.222246
 
2.6%
Other values (9813)604956
72.0%
ValueCountFrequency (%)
-1.11
 
< 0.1%
-0.761
 
< 0.1%
-0.21
 
< 0.1%
0.21
 
< 0.1%
1.51
 
< 0.1%
1.81
 
< 0.1%
2.21
 
< 0.1%
2.41
 
< 0.1%
3.23
< 0.1%
3.32
< 0.1%
ValueCountFrequency (%)
33.21
< 0.1%
32.3011
< 0.1%
32.121
< 0.1%
32.12
< 0.1%
321
< 0.1%
31.8541
< 0.1%
31.51
< 0.1%
31.41
< 0.1%
31.11
< 0.1%
31.0281
< 0.1%

diameter
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing702055
Missing (%)83.6%
Memory size6.4 MiB

extent
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct18
Distinct (%)100.0%
Missing839718
Missing (%)> 99.9%
Memory size6.4 MiB
964.4 x 964.2 x 891.8
 
1
1.11 x 0.53 x 0.50
 
1
0.565 x 0.535 x 0.508
 
1
1.532 x 1.495 x 1.347
 
1
0.535x0.294x0.209
 
1
Other values (13)
13 

Length

Max length21
Median length17
Mean length15.83333333
Min length9

Characters and Unicode

Total characters285
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st row964.4 x 964.2 x 891.8
2nd row582x556x500
3rd row572.6 x 557.2 x 446.4
4th row279 x 232 x 189
5th row276x94x78

Common Values

ValueCountFrequency (%)
964.4 x 964.2 x 891.81
 
< 0.1%
1.11 x 0.53 x 0.501
 
< 0.1%
0.565 x 0.535 x 0.5081
 
< 0.1%
1.532 x 1.495 x 1.3471
 
< 0.1%
0.535x0.294x0.2091
 
< 0.1%
2.1x1.0x1.01
 
< 0.1%
6.6 x 5.0 x 3.41
 
< 0.1%
2.35 x 1.65 x 1.441
 
< 0.1%
1.70x2.03x4.261
 
< 0.1%
5.0x2.0x2.11
 
< 0.1%
Other values (8)8
 
< 0.1%
(Missing)839718
> 99.9%

Length

2022-10-02T00:00:59.571061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
x18
33.3%
276x94x781
 
1.9%
1.441
 
1.9%
1.70x2.03x4.261
 
1.9%
5.0x2.0x2.11
 
1.9%
582x556x5001
 
1.9%
18.2x10.5x8.91
 
1.9%
34.4x11.2x11.21
 
1.9%
59.8x25.4x18.61
 
1.9%
964.41
 
1.9%
Other values (27)27
50.0%

Most occurring characters

ValueCountFrequency (%)
.45
15.8%
36
12.6%
x36
12.6%
525
8.8%
024
8.4%
222
7.7%
122
7.7%
418
 
6.3%
915
 
5.3%
613
 
4.6%
Other values (3)29
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number168
58.9%
Other Punctuation45
 
15.8%
Space Separator36
 
12.6%
Lowercase Letter36
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
525
14.9%
024
14.3%
222
13.1%
122
13.1%
418
10.7%
915
8.9%
613
7.7%
811
6.5%
311
6.5%
77
 
4.2%
Other Punctuation
ValueCountFrequency (%)
.45
100.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Lowercase Letter
ValueCountFrequency (%)
x36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common249
87.4%
Latin36
 
12.6%

Most frequent character per script

Common
ValueCountFrequency (%)
.45
18.1%
36
14.5%
525
10.0%
024
9.6%
222
8.8%
122
8.8%
418
 
7.2%
915
 
6.0%
613
 
5.2%
811
 
4.4%
Other values (2)18
 
7.2%
Latin
ValueCountFrequency (%)
x36
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.45
15.8%
36
12.6%
x36
12.6%
525
8.8%
024
8.4%
222
7.7%
122
7.7%
418
 
6.3%
915
 
5.3%
613
 
4.6%
Other values (3)29
10.2%

albedo
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1057
Distinct (%)0.8%
Missing703284
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean0.1300516255
Minimum0.001
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:59.679090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.034
Q10.053
median0.078
Q30.188
95-th percentile0.349
Maximum1
Range0.999
Interquartile range (IQR)0.135

Descriptive statistics

Standard deviation0.1099834654
Coefficient of variation (CV)0.8456908168
Kurtosis3.954345613
Mean0.1300516255
Median Absolute Deviation (MAD)0.035
Skewness1.695329378
Sum17745.8044
Variance0.01209636266
MonotonicityNot monotonic
2022-10-02T00:00:59.800821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0571669
 
0.2%
0.0551655
 
0.2%
0.0531650
 
0.2%
0.0521636
 
0.2%
0.0491629
 
0.2%
0.0511628
 
0.2%
0.0561616
 
0.2%
0.0541606
 
0.2%
0.0481606
 
0.2%
0.0591592
 
0.2%
Other values (1047)120165
 
14.3%
(Missing)703284
83.8%
ValueCountFrequency (%)
0.0015
< 0.1%
0.0042
 
< 0.1%
0.0053
 
< 0.1%
0.0064
< 0.1%
0.0073
 
< 0.1%
0.0084
< 0.1%
0.0094
< 0.1%
0.018
< 0.1%
0.0118
< 0.1%
0.01161
 
< 0.1%
ValueCountFrequency (%)
124
< 0.1%
0.9941
 
< 0.1%
0.9882
 
< 0.1%
0.9871
 
< 0.1%
0.9841
 
< 0.1%
0.9771
 
< 0.1%
0.9751
 
< 0.1%
0.9661
 
< 0.1%
0.963
 
< 0.1%
0.9591
 
< 0.1%

rot_per
Real number (ℝ≥0)

MISSING

Distinct11230
Distinct (%)59.7%
Missing820940
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean21.1367719
Minimum0.004389
Maximum3240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:00:59.926050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.004389
5-th percentile2.425825
Q14.21
median6.653
Q312.62425
95-th percentile73.9775
Maximum3240
Range3239.995611
Interquartile range (IQR)8.41425

Descriptive statistics

Standard deviation73.13175091
Coefficient of variation (CV)3.459929987
Kurtosis344.4887443
Mean21.1367719
Median Absolute Deviation (MAD)3.153
Skewness14.03914359
Sum397286.7646
Variance5348.252992
MonotonicityNot monotonic
2022-10-02T00:01:00.049538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1256
 
< 0.1%
2450
 
< 0.1%
149
 
< 0.1%
1.3347
 
< 0.1%
636
 
< 0.1%
835
 
< 0.1%
1034
 
< 0.1%
1.9732
 
< 0.1%
2030
 
< 0.1%
1.7829
 
< 0.1%
Other values (11220)18398
 
2.2%
(Missing)820940
97.8%
ValueCountFrequency (%)
0.0043891
< 0.1%
0.00491
< 0.1%
0.00682951
< 0.1%
0.00831
< 0.1%
0.00857991
< 0.1%
0.00863091
< 0.1%
0.0094381
< 0.1%
0.011851
< 0.1%
0.0121
< 0.1%
0.01271
< 0.1%
ValueCountFrequency (%)
32401
< 0.1%
1917.2211
< 0.1%
18801
< 0.1%
16411
< 0.1%
15611
< 0.1%
13321
< 0.1%
13201
< 0.1%
12651
< 0.1%
1256.0161
< 0.1%
1234.1711
< 0.1%

GM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct14
Distinct (%)100.0%
Missing839722
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7.821927596
Minimum2.1 × 10-9
Maximum62.6284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:01:00.155469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.1 × 10-9
5-th percentile4.115 × 10-9
Q10.001022225
median0.61925
Q36.5
95-th percentile33.48994
Maximum62.6284
Range62.6284
Interquartile range (IQR)6.498977775

Descriptive statistics

Standard deviation16.78880479
Coefficient of variation (CV)2.146376911
Kurtosis10.12039438
Mean7.821927596
Median Absolute Deviation (MAD)0.6192499964
Skewness3.07227767
Sum109.5069863
Variance281.8639662
MonotonicityNot monotonic
2022-10-02T00:01:00.234490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
62.62841
 
< 0.1%
14.31
 
< 0.1%
17.81
 
< 0.1%
71
 
< 0.1%
1.531
 
< 0.1%
0.4911
 
< 0.1%
0.74751
 
< 0.1%
0.002751
 
< 0.1%
0.006891
 
< 0.1%
0.00044631
 
< 0.1%
Other values (4)4
 
< 0.1%
(Missing)839722
> 99.9%
ValueCountFrequency (%)
2.1 × 10-91
< 0.1%
5.2 × 10-91
< 0.1%
3.224 × 10-81
< 0.1%
0.00044631
< 0.1%
0.002751
< 0.1%
0.006891
< 0.1%
0.4911
< 0.1%
0.74751
< 0.1%
1.531
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
62.62841
< 0.1%
17.81
< 0.1%
14.31
< 0.1%
71
< 0.1%
51
< 0.1%
1.531
< 0.1%
0.74751
< 0.1%
0.4911
< 0.1%
0.006891
< 0.1%
0.002751
< 0.1%

BV
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct307
Distinct (%)30.1%
Missing838715
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean0.7692115573
Minimum0.58
Maximum1.077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:01:00.342594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile0.65
Q10.7
median0.743
Q30.85
95-th percentile0.915
Maximum1.077
Range0.497
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.08862520944
Coefficient of variation (CV)0.1152156498
Kurtosis-0.7164569799
Mean0.7692115573
Median Absolute Deviation (MAD)0.063
Skewness0.4156207948
Sum785.365
Variance0.007854427749
MonotonicityNot monotonic
2022-10-02T00:01:00.587971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8613
 
< 0.1%
0.6913
 
< 0.1%
0.7213
 
< 0.1%
0.8512
 
< 0.1%
0.69811
 
< 0.1%
0.70511
 
< 0.1%
0.8410
 
< 0.1%
0.71910
 
< 0.1%
0.87110
 
< 0.1%
0.70810
 
< 0.1%
Other values (297)908
 
0.1%
(Missing)838715
99.9%
ValueCountFrequency (%)
0.581
 
< 0.1%
0.591
 
< 0.1%
0.5951
 
< 0.1%
0.5981
 
< 0.1%
0.6031
 
< 0.1%
0.6041
 
< 0.1%
0.613
< 0.1%
0.6111
 
< 0.1%
0.6131
 
< 0.1%
0.6151
 
< 0.1%
ValueCountFrequency (%)
1.0771
< 0.1%
1.0591
< 0.1%
1.0471
< 0.1%
1.0261
< 0.1%
1.0221
< 0.1%
1.011
< 0.1%
0.992
< 0.1%
0.9641
< 0.1%
0.962
< 0.1%
0.9551
< 0.1%

UB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct339
Distinct (%)34.6%
Missing838757
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean0.3643963228
Minimum0.12
Maximum0.655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:01:00.715939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.2239
Q10.289
median0.36
Q30.439
95-th percentile0.5211
Maximum0.655
Range0.535
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.09578029684
Coefficient of variation (CV)0.2628464967
Kurtosis-0.7169325515
Mean0.3643963228
Median Absolute Deviation (MAD)0.075
Skewness0.1681964241
Sum356.744
Variance0.009173865263
MonotonicityNot monotonic
2022-10-02T00:01:00.837087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4114
 
< 0.1%
0.3912
 
< 0.1%
0.511
 
< 0.1%
0.3611
 
< 0.1%
0.3810
 
< 0.1%
0.379
 
< 0.1%
0.3759
 
< 0.1%
0.349
 
< 0.1%
0.4258
 
< 0.1%
0.448
 
< 0.1%
Other values (329)878
 
0.1%
(Missing)838757
99.9%
ValueCountFrequency (%)
0.121
 
< 0.1%
0.141
 
< 0.1%
0.162
< 0.1%
0.1611
 
< 0.1%
0.1661
 
< 0.1%
0.171
 
< 0.1%
0.1771
 
< 0.1%
0.181
 
< 0.1%
0.1881
 
< 0.1%
0.1893
< 0.1%
ValueCountFrequency (%)
0.6551
< 0.1%
0.6331
< 0.1%
0.6151
< 0.1%
0.611
< 0.1%
0.6021
< 0.1%
0.6011
< 0.1%
0.5961
< 0.1%
0.5941
< 0.1%
0.5881
< 0.1%
0.581
< 0.1%

IR
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing839735
Missing (%)> 99.9%
Memory size6.4 MiB
-0.33

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row-0.33

Common Values

ValueCountFrequency (%)
-0.331
 
< 0.1%
(Missing)839735
> 99.9%

Length

2022-10-02T00:01:00.942315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T00:01:01.059603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.331
100.0%

Most occurring characters

ValueCountFrequency (%)
32
40.0%
-1
20.0%
01
20.0%
.1
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3
60.0%
Dash Punctuation1
 
20.0%
Other Punctuation1
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32
66.7%
01
33.3%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
32
40.0%
-1
20.0%
01
20.0%
.1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
40.0%
-1
20.0%
01
20.0%
.1
20.0%

spec_B
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)2.0%
Missing838070
Missing (%)99.8%
Memory size6.4 MiB
S
445 
C
152 
Ch
139 
X
138 
Sq
114 
Other values (29)
678 

Length

Max length5
Median length1
Mean length1.404561825
Min length1

Characters and Unicode

Total characters2340
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowC
2nd rowB
3rd rowSk
4th rowV
5th rowS

Common Values

ValueCountFrequency (%)
S445
 
0.1%
C152
 
< 0.1%
Ch139
 
< 0.1%
X138
 
< 0.1%
Sq114
 
< 0.1%
Xc67
 
< 0.1%
B66
 
< 0.1%
Sl56
 
< 0.1%
V48
 
< 0.1%
Xk48
 
< 0.1%
Other values (24)393
 
< 0.1%
(Missing)838070
99.8%

Length

2022-10-02T00:01:01.137716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s461
27.7%
c155
 
9.3%
x144
 
8.6%
ch139
 
8.3%
sq116
 
7.0%
xc67
 
4.0%
b66
 
4.0%
sl56
 
3.4%
v49
 
2.9%
xk48
 
2.9%
Other values (18)365
21.9%

Most occurring characters

ValueCountFrequency (%)
S728
31.1%
C355
15.2%
X289
 
12.4%
h154
 
6.6%
q116
 
5.0%
k77
 
3.3%
c67
 
2.9%
B66
 
2.8%
l56
 
2.4%
L56
 
2.4%
Other values (19)376
16.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1668
71.3%
Lowercase Letter641
 
27.4%
Other Punctuation29
 
1.2%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S728
43.6%
C355
21.3%
X289
 
17.3%
B66
 
4.0%
L56
 
3.4%
V50
 
3.0%
K38
 
2.3%
Q20
 
1.2%
T19
 
1.1%
A17
 
1.0%
Other values (5)30
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
h154
24.0%
q116
18.1%
k77
12.0%
c67
10.5%
l56
 
8.7%
a38
 
5.9%
b36
 
5.6%
e30
 
4.7%
r27
 
4.2%
g25
 
3.9%
Other Punctuation
ValueCountFrequency (%)
:29
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2309
98.7%
Common31
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S728
31.5%
C355
15.4%
X289
 
12.5%
h154
 
6.7%
q116
 
5.0%
k77
 
3.3%
c67
 
2.9%
B66
 
2.9%
l56
 
2.4%
L56
 
2.4%
Other values (16)345
14.9%
Common
ValueCountFrequency (%)
:29
93.5%
(1
 
3.2%
)1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S728
31.1%
C355
15.2%
X289
 
12.4%
h154
 
6.6%
q116
 
5.0%
k77
 
3.3%
c67
 
2.9%
B66
 
2.8%
l56
 
2.4%
L56
 
2.4%
Other values (19)376
16.1%

spec_T
Categorical

HIGH CARDINALITY
MISSING

Distinct131
Distinct (%)13.4%
Missing838756
Missing (%)99.9%
Memory size6.4 MiB
S
338 
C
140 
X
52 
M
38 
D
 
35
Other values (126)
377 

Length

Max length6
Median length1
Mean length1.410204082
Min length1

Characters and Unicode

Total characters1382
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)7.2%

Sample

1st rowG
2nd rowB
3rd rowS
4th rowV
5th rowS

Common Values

ValueCountFrequency (%)
S338
 
< 0.1%
C140
 
< 0.1%
X52
 
< 0.1%
M38
 
< 0.1%
D35
 
< 0.1%
P33
 
< 0.1%
F28
 
< 0.1%
XC23
 
< 0.1%
CX21
 
< 0.1%
E12
 
< 0.1%
Other values (121)260
 
< 0.1%
(Missing)838756
99.9%

Length

2022-10-02T00:01:01.241491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s338
34.5%
c150
15.3%
x53
 
5.4%
m38
 
3.9%
d35
 
3.6%
p34
 
3.5%
f29
 
3.0%
cx27
 
2.8%
xc24
 
2.4%
e12
 
1.2%
Other values (97)240
24.5%

Most occurring characters

ValueCountFrequency (%)
S382
27.6%
C305
22.1%
X151
 
10.9%
D84
 
6.1%
F78
 
5.6%
U78
 
5.6%
P66
 
4.8%
:64
 
4.6%
M43
 
3.1%
B32
 
2.3%
Other values (9)99
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1318
95.4%
Other Punctuation64
 
4.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S382
29.0%
C305
23.1%
X151
 
11.5%
D84
 
6.4%
F78
 
5.9%
U78
 
5.9%
P66
 
5.0%
M43
 
3.3%
B32
 
2.4%
G31
 
2.4%
Other values (8)68
 
5.2%
Other Punctuation
ValueCountFrequency (%)
:64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1318
95.4%
Common64
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S382
29.0%
C305
23.1%
X151
 
11.5%
D84
 
6.4%
F78
 
5.9%
U78
 
5.9%
P66
 
5.0%
M43
 
3.3%
B32
 
2.4%
G31
 
2.4%
Other values (8)68
 
5.2%
Common
ValueCountFrequency (%)
:64
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S382
27.6%
C305
22.1%
X151
 
10.9%
D84
 
6.1%
F78
 
5.6%
U78
 
5.6%
P66
 
4.8%
:64
 
4.6%
M43
 
3.1%
B32
 
2.3%
Other values (9)99
 
7.2%

neo
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size1.6 MiB
False
818341 
True
 
21389
(Missing)
 
6
ValueCountFrequency (%)
False818341
97.5%
True21389
 
2.5%
(Missing)6
 
< 0.1%
2022-10-02T00:01:01.342255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

pha
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing16922
Missing (%)2.0%
Memory size1.6 MiB
False
820800 
True
 
2014
(Missing)
 
16922
ValueCountFrequency (%)
False820800
97.7%
True2014
 
0.2%
(Missing)16922
 
2.0%
2022-10-02T00:01:01.434091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

moid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct295369
Distinct (%)35.9%
Missing16922
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1.423511209
Minimum3.43764 × 10-7
Maximum79.5013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2022-10-02T00:01:01.541014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.43764 × 10-7
5-th percentile0.7068403
Q10.9785395
median1.23788
Q31.59061
95-th percentile1.9536635
Maximum79.5013
Range79.50129966
Interquartile range (IQR)0.6120705

Descriptive statistics

Standard deviation2.251047729
Coefficient of variation (CV)1.581334741
Kurtosis244.232168
Mean1.423511209
Median Absolute Deviation (MAD)0.2982415
Skewness15.1641912
Sum1171284.952
Variance5.067215879
MonotonicityNot monotonic
2022-10-02T00:01:01.663115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0398921
 
< 0.1%
1.105521
 
< 0.1%
1.0753620
 
< 0.1%
1.0287520
 
< 0.1%
1.0184320
 
< 0.1%
1.039320
 
< 0.1%
1.0577619
 
< 0.1%
1.0869619
 
< 0.1%
1.0631319
 
< 0.1%
1.0970119
 
< 0.1%
Other values (295359)822616
98.0%
(Missing)16922
 
2.0%
ValueCountFrequency (%)
3.43764 × 10-71
< 0.1%
4.43146 × 10-71
< 0.1%
4.54412 × 10-71
< 0.1%
1.4097 × 10-61
< 0.1%
1.62715 × 10-61
< 0.1%
2.78487 × 10-61
< 0.1%
3.60874 × 10-61
< 0.1%
4.35092 × 10-61
< 0.1%
4.61835 × 10-61
< 0.1%
4.77024 × 10-61
< 0.1%
ValueCountFrequency (%)
79.50131
< 0.1%
75.18391
< 0.1%
64.11031
< 0.1%
54.93061
< 0.1%
50.69671
< 0.1%
50.39961
< 0.1%
49.9731
< 0.1%
49.04231
< 0.1%
48.39591
< 0.1%
47.7661
< 0.1%

Interactions

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2022-10-02T00:00:27.184635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:29.560530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-01T23:59:16.073973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T23:59:22.512021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-01T23:59:47.322132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-01T23:59:36.655823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T23:59:41.859159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T23:59:47.850053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T23:59:53.652394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-01T23:59:59.981942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:07.499597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:13.014835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:18.511675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:21.217928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:23.637914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T00:00:27.650269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T00:00:30.068119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-02T00:01:01.792994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T00:01:01.984483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T00:01:02.174530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T00:01:02.348341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-02T00:01:02.620137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T00:00:36.637324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T00:00:41.826262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-02T00:00:51.961132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-02T00:00:53.404070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

full_nameaeGiomwqadper_ydata_arccondition_coden_obs_usedHdiameterextentalbedorot_perGMBVUBIRspec_Bspec_Tneophamoid
01 Ceres2.7691650.0760090.1210.59406780.30553273.5976942.5586842.9796474.6082028822.0010023.34939.4964.4 x 964.2 x 891.80.09009.07417062.62840.7130.426NaNCGNN1.594780
12 Pallas2.7724660.2303370.1134.836234173.080063310.0488572.1338653.4110674.61644472318.0084904.13545582x556x5000.10107.81320014.30000.6350.284NaNBBNN1.233240
23 Juno2.6691500.2569420.3212.988919169.852760248.1386261.9833323.3549674.36081472684.0071045.33246.596NaN0.21407.210000NaN0.8240.433NaNSkSNN1.034540
34 Vesta2.3614180.0887210.327.141771103.810804150.7285412.1519092.5709263.62883724288.0093253.20525.4572.6 x 557.2 x 446.40.42285.34212817.80000.7820.492NaNVVNN1.139480
45 Astraea2.5742490.191095NaN5.366988141.576604358.6876082.0823243.0661744.13032363431.0028616.85106.699NaN0.274016.806000NaN0.8260.411NaNSSNN1.095890
56 Hebe2.4251600.2030070.2414.737901138.640203239.8074901.9328352.9174853.77675562329.0060345.71185.18NaN0.26797.274500NaN0.8220.399NaNSSNN0.973965
67 Iris2.3853340.231206NaN5.523651259.563231145.2651061.8338312.9368373.68410562452.0052065.51199.83NaN0.27667.139000NaN0.8550.484NaNSSNN0.846100
78 Flora2.2017640.1564990.285.886955110.889330285.2874621.8571902.5463393.26711562655.0027446.49147.491NaN0.226012.865000NaN0.8850.489NaNNaNSNN0.874176
89 Metis2.3856370.1231140.175.57681668.9085776.4173692.0919312.6793423.68480661821.0026496.28190NaN0.11805.079000NaN0.8580.496NaNNaNSNN1.106910
910 Hygiea3.1415390.112461NaN3.831560283.202167312.3152062.7882403.4948395.56829162175.0034095.43407.12NaN0.071727.6300007.00000.6960.351NaNCCNN1.778390

Last rows

full_nameaeGiomwqadper_ydata_arccondition_coden_obs_usedHdiameterextentalbedorot_perGMBVUBIRspec_Bspec_Tneophamoid
839726(4532 P-L)2.3245780.200505NaN6.62679817.773486328.8677311.8584872.7906693.5442504.09517.393NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.855072
839727(4717 P-L)2.9537860.219023NaN4.7967154.018721345.9662822.3068393.6007345.0766454.091016.280NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN1.300570
839728(4847 P-L)2.7215960.238416NaN7.98416514.097989355.4617912.0727243.3704684.4899724.09716.827NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN1.071900
839729(6013 P-L)2.2797250.185817NaN5.976037194.238895195.3148181.8561142.7033363.4421664.09617.100NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.861240
839730(6331 P-L)2.3348030.282830NaN8.082044355.2503035.2636991.6744502.9951563.56766120863.008318.500NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.670508
839731(6344 P-L)2.8129450.664688NaN4.695700183.310012234.6183520.9432144.6826764.71791417298.0011820.400NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNYY0.032397
839732(1168 T-2)2.6452380.259376NaN12.5749371.620020339.5680721.9591263.3313504.30234616.091517.507NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.956145
839733(2060 T-2)2.3731370.202053NaN0.732484176.499082198.0265271.8936382.8526363.6558845.09618.071NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.893896
839734(2678 T-3)2.2604040.258348NaN9.661947204.512448148.4969881.6764332.8443763.39850110.091318.060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.680220
839735(4571 T-3)2.5464420.287672NaN5.35623870.709555273.4832651.8139013.2789834.06358011.091117.406NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNN0.815280